Financial Signal ML Engineer

New

Skills

Machine Learning Python

Join our innovative team as we develop a cutting-edge system to identify emergent behavioral signals from global financial market data. This role focuses on unsupervised exploration of cross-asset patterns, leveraging machine learning to deliver standardized, transparent, and high-quality financial data. You will contribute to designing models that simplify complex market signals, supporting AI-driven financial analytics in a highly collaborative and intellectually stimulating environment.

Key Responsibilities
  • Design and implement unsupervised machine learning models for abnormality and early signal detection in financial time series data.
  • Conduct advanced feature engineering using multi-parametric methods, including rolling windows and volatility analysis.
  • Develop and apply clustering techniques to group and analyze cross-asset patterns.
  • Evaluate and score detected signals based on impact and anomaly strength metrics.
  • Generate clear, LLM-based textual descriptions of identified signals for end-user interpretability.
Required Skills & Qualifications
  • Proven experience with end-of-day (EOD) financial data handling and normalization.
  • Strong background in time series analysis and familiarity with financial product characteristics.
  • Hands-on expertise in unsupervised ML techniques such as Isolation Forest, One-Class SVM, Autoencoders, and DBSCAN.
  • Experience with supervised ML, lookahead modeling, and transformer architectures for early signal detection.
  • Proficiency in feature engineering, clustering, and correlation-based distance metrics.
  • Understanding of signal recognition frameworks and evaluation metrics within financial contexts.
  • Ability to articulate complex signals into clear textual summaries using large language models (LLMs).

Job Type: Remote

Salary: Not Disclosed

Experience: Entry

Duration: 12 Months

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